skfolio.preprocessing.CSWinsorizer#

class skfolio.preprocessing.CSWinsorizer(*, low=0.01, high=0.99)[source]#

Cross-sectional winsorization.

Clips each finite value within an observation to the interval between the low and high percentiles of that observation’s cross-section.

NaNs are treated as missing values. They are ignored when computing cross-sectional percentiles and are preserved in the output.

When cs_weights is provided, percentile boundaries are computed on the estimation universe, defined by cs_weights > 0. Assets outside the estimation universe still receive clipped values using those boundaries. For this estimator, cs_weights is used only to define the estimation universe; percentile estimation itself remains equal-weighted over the selected assets.

This transformer is stateless.

Parameters:
lowfloat, default=0.01

Lower percentile used for clipping. Must satisfy \(0 \le \text{low} < \text{high} \le 1\).

highfloat, default=0.99

Upper percentile used for clipping. Must satisfy \(0 \le \text{low} < \text{high} \le 1\).

Methods

fit(X[, y, cs_weights, cs_groups])

Fit the transformer.

fit_transform(X[, y, cs_weights, cs_groups])

Fit to X and return the transformed values.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_fit_request(*[, cs_groups, cs_weights])

Configure whether metadata should be requested to be passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_transform_request(*[, cs_groups, cs_weights])

Configure whether metadata should be requested to be passed to the transform method.

transform(X[, cs_weights, cs_groups])

Winsorize each observation to low/high percentiles.

See also

CSTanhShrinker

Smoothly shrinks extreme values.

Examples

>>> import numpy as np
>>> from skfolio.preprocessing import CSWinsorizer
>>>
>>> X = np.array([[1.0, np.nan, 3.0, 4.0],
...               [4.0, 3.0, 2.0, 1.0],
...               [10.0, 20.0, np.nan, 40.0]])
>>>
>>> transformer = CSWinsorizer(low=0.1, high=0.9)
>>> transformer.fit_transform(X)
array([[ 1.4,  nan,  3. ,  3.8],
       [ 3.7,  3. ,  2. ,  1.3],
       [12. , 20. ,  nan, 36. ]])
>>>
>>> # Use cs_weights for the estimation universe before computing the clip bounds.
>>> cs_weights = np.array([[1.0, 0.0, 1.0, 1.0],
...                        [1.0, 0.0, 1.0, 1.0],
...                        [1.0, 1.0, 0.0, 1.0]])
>>>
>>> transformer.fit_transform(X, cs_weights=cs_weights)
array([[ 1.4,  nan,  3. ,  3.8],
       [ 3.6,  3. ,  2. ,  1.2],
       [12. , 20. ,  nan, 36. ]])
fit(X, y=None, cs_weights=None, cs_groups=None)#

Fit the transformer.

Cross-sectional transformers are stateless and do not learn data-dependent parameters. This method validates the estimator parameters, validates X, and records n_features_in_ for scikit-learn compatibility.

Parameters:
Xarray-like of shape (n_observations, n_assets)

Input matrix where each row is an observation and each column is an asset.

yIgnored

Not used, present for API consistency by convention.

cs_weightsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional weights accepted for API consistency with transform. They are ignored during fitting.

cs_groupsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional group labels accepted for API consistency with transform. They are ignored during fitting.

Returns:
selfBaseCSTransformer

Fitted estimator.

fit_transform(X, y=None, cs_weights=None, cs_groups=None)#

Fit to X and return the transformed values.

Parameters:
Xarray-like of shape (n_observations, n_assets)

Input matrix where each row is an observation and each column is an asset.

yIgnored

Not used, present for API consistency by convention.

cs_weightsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional weights forwarded to transform.

cs_groupsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional group labels forwarded to transform.

Returns:
X_newndarray of shape (n_observations, n_assets)

Transformed array.

get_feature_names_out(input_features=None)#

Get output feature names for transformation.

Parameters:
input_featuresarray-like of str or None, default=None

Input features.

  • If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

  • If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:
feature_names_outndarray of str objects

Same as input features.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

set_fit_request(*, cs_groups='$UNCHANGED$', cs_weights='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
cs_groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_groups parameter in fit.

cs_weightsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_weights parameter in fit.

Returns:
selfobject

The updated object.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_transform_request(*, cs_groups='$UNCHANGED$', cs_weights='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
cs_groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_groups parameter in transform.

cs_weightsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_weights parameter in transform.

Returns:
selfobject

The updated object.

transform(X, cs_weights=None, cs_groups=None)[source]#

Winsorize each observation to low/high percentiles.

Parameters:
Xarray-like of shape (n_observations, n_assets)

Input matrix where each row is an observation and each column is an asset. NaNs are allowed and preserved.

cs_weightsarray-like of shape (n_observations, n_assets), optional

Optional non-negative cross-sectional weights used only to define the estimation universe through the convention cs_weights > 0. Percentile boundaries are then estimated in an equal-weighted way over the selected assets. Non-estimation assets still receive clipped values using those boundaries. If None, all finite assets are used to compute percentiles.

cs_groupsarray-like of shape (n_observations, n_assets), optional

Not used, present for API consistency by convention.

Returns:
X_clippedndarray of shape (n_observations, n_assets)

Winsorized values. NaN values from the input are preserved.

Raises:
ValueError

If low / high are invalid, X is not a non-empty 2D array, cs_weights is invalid, or any observation has no estimation asset.